A Surprising Thing: The Application of Machine Learning Ensembles and Signal Theory to Predict Earnings Surprises

103 Pages Posted: 17 Jul 2019 Last revised: 25 Mar 2020

See all articles by Derek Snow

Derek Snow

The Alan Turing Institute; New York University (NYU) - Finance and Risk Engineering Department; University of Auckland

Date Written: July 27, 2017

Abstract

Nonlinear classification models can predict future earnings surprises with a high accuracy by using pricing and earnings input data. Surprises of 15% or more can be predicted with 71% accuracy. These predictions can be used to form profitable trading strategies. Additional variables have been created using signal-processing and handcrafted feature-engineering methods. Some of these variables have in the past been known to be related to analyst bias. The machine learning model in effect corrects for analyst mistakes and biases by incorporating these variables into a nonlinear prediction model to predict future earnings surprises.

Keywords: Machine Learning, Earnings Surprise, Event-driven, Trading Strategy, Prediction

JEL Classification: C32, C38, C45, G14

Suggested Citation

Snow, Derek, A Surprising Thing: The Application of Machine Learning Ensembles and Signal Theory to Predict Earnings Surprises (July 27, 2017). Available at SSRN: https://ssrn.com/abstract=3420722 or http://dx.doi.org/10.2139/ssrn.3420722

Derek Snow (Contact Author)

The Alan Turing Institute ( email )

British Library, 96 Euston Rd
London, NW1 2DB
United Kingdom

HOME PAGE: http://https://www.turing.ac.uk/

New York University (NYU) - Finance and Risk Engineering Department ( email )

6 Metrotech Center
New York, NY 11201
United States

University of Auckland ( email )

Private Bag 92019
Auckland Mail Centre
Auckland, 1142
New Zealand

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